System and method for motor fault detection using stator current noise cancellation

ABSTRACT

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation-in-part of, and claimspriority to, U.S. non-provisional application Ser. No. 12/132,056, filedJun. 3, 2008, and U.S. provisional application Ser. No. 60/932,742,filed Jun. 4, 2007, which are both incorporated herein by reference.

GOVERNMENT LICENSE RIGHTS

The present invention was made at least in part with Government supportunder Contract No. DE-FC36-04GO14000, awarded by the United StatesDepartment of Energy. The Government may have certain rights in theinvention.

BACKGROUND OF THE INVENTION

The present invention relates generally to motors and, moreparticularly, to a system and method for detection of incipientconditions indicative of motor faults.

Three-phase induction motors consume a large percentage of generatedelectricity capacity. Many applications for this “workhorse” of industryare fan and pump industrial applications. For example, in a typicalintegrated paper mill, low voltage and medium voltage motors maycomprise nearly 70% of all driven electrical loads. Due to theprevalence of these motors in industry, it is paramount that thethree-phase motor be reliable. Industry reliability surveys suggest thatmotor failures typically fall into one of four major categories.Specifically, motor faults typically result from bearing failure, statorturn faults, rotor bar failure, or other general faults/failures. Withinthese four categories: bearing, stator, and rotor failure account forapproximately 85% of all motor failures.

It is believed that this percentage could be significantly reduced ifthe driven equipment were better aligned when installed, and remainedaligned regardless of changes in operating conditions. However, motorsare often coupled to misaligned pump loads or loads with rotationalunbalance and fail prematurely due to stresses imparted upon the motorbearings. Furthermore, manually detecting such fault causing conditionsis difficult at best because doing so requires the motor to be running.As such, an operator is usually required to remove the motor fromoperation to perform a maintenance review and diagnosis. However,removing the motor from service is undesirable in many applicationsbecause motor down-time can be extremely costly.

As such, some detection devices have been designed that generatefeedback regarding an operating motor. The feedback is then reviewed byan operator to determine the operating conditions of the motor. However,most systems that monitor operating motors merely provide feedback offaults that have likely already damaged the motor. As such, thoughoperational feedback is sent to the operator, it is usually too late forpreventive action to be taken.

Some systems have attempted to provide an operator with early faultwarning feedback. For example, vibration monitoring has been utilized toprovide some early misalignment or unbalance-based faults. However, whena mechanical resonance occurs, machine vibrations are amplified. Due tothis amplification, false positives indicating severe mechanicalasymmetry are possible. Furthermore, vibration-based monitoring systemstypically require highly invasive and specialized monitoring systems tobe deployed within the motor system.

In light of the drawbacks of vibration-based monitoring, current-basedmonitoring techniques have been developed to provide a more inexpensive,non-intrusive technique for detecting bearing faults. There are alsolimitations and drawbacks to present current-based fault detection. Thatis, in current-based bearing fault detection, it can be challenging toextract a fault signature from the motor stator current. For differenttypes of bearing faults, fault signatures can be in different forms.According to general fault development processes, bearing faults can becategorized as single-point defects or generalized roughness. Mostcurrent-based bearing fault detection techniques currently in use todayare directed toward detecting single-point defects and rely on locatingand processing the characteristic bearing fault frequencies in thestator current. Such techniques, however, may not be suitable fordetecting generalized roughness faults. That is, generalized-roughnessfaults exhibit degraded bearing surfaces, but not necessarilydistinguished defects and, therefore, characteristic fault frequenciesmay not necessarily exist in the stator current. As many bearing faultsinitially develop as generalized-roughness bearing faults, especially atan early stage, it would be beneficial for current-based bearing faultdetection techniques to be able to detect such generalized-roughnessbearing faults.

It would therefore be desirable to design a current-based bearing faultdetection technique that overcomes the aforementioned drawbacks. Acurrent-based bearing fault detection technique that allows fordetection of generalized-roughness bearing faults would be beneficial,by providing early stage detection of bearing faults.

BRIEF DESCRIPTION OF THE INVENTION

The present invention provides a system and method for detectingimpending mechanical motor faults by way of current noise cancellation.Current data is decomposed into a non-fault component (i.e., noise) anda fault component, and noise-cancellation is performed to isolate thefault component of the current and generate a fault identifier.

In accordance with one aspect of the invention, a controller configuredto detect indicia of incipient mechanical motor faults includes aprocessor programmed to receive a first set of real-time operatingcurrent data from a motor during operation, define a noise componentpresent in the first set of real-time operating current data, andgenerate a fault index for the first set of real-time operating currentdata based on any isolated fault components. The processor is furtherprogrammed to acquire at least one additional set of real-time operatingcurrent data from the motor during operation, redefine the noisecomponent present in each of the at least one additional sets ofreal-time operating current data, remove the redefined noise componentfrom each of the at least one additional sets of real-time operatingcurrent data to identify any fault components present therein, andgenerate a fault index for each of the at least one additional sets ofreal-time operating current data based on any isolated fault components.

In accordance with another aspect of the invention, a non-invasivemethod for detecting impending faults in electric machines includesacquiring a plurality of stator current data sets from the electricmachine during operation, configuring a current data filter for each ofthe plurality of stator current data sets, and applying each of thecurrent data filters to its respective stator current data set inreal-time to generate a noise-cancelled stator current. The method alsoincludes determining a fault index from the noise-cancelled statorcurrent for each of the plurality of stator current data sets,monitoring a value of the fault index for each of the plurality ofstator current data sets, and generating an alert if the value of apre-determined number of fault indices exceeds a control limit.

In accordance with yet another aspect of the invention, a system formonitoring current to predict faults includes at least one non-invasivecurrent sensor configured to acquire stator current data from anoperating motor. The system also includes a processor connected toreceive the stator current data from the at least one non-invasivecurrent sensor. The processor is programmed to repeatedly receive a setof real-time operating current data from the at least one non-invasivecurrent sensor, where the set of real-time operating data isrepresentative of real-time motor operation. The processor is furtherprogrammed to define a non-fault component from each of the repeatedlyreceived sets of real-time operating current data, the non-faultcomponent being a periodic component of the real-time operating currentdata, and remove the non-fault component from each of the sets ofreal-time operating current data in real-time to isolate residualcurrent data. The processor is also programmed to process the residualcurrent data to identify possible bearing faults, generate a fault indexfor any identified bearing faults, and generate an alert if the faultindex exceeds a fault index threshold.

Various other features and advantages of the present invention will bemade apparent from the following detailed description and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate preferred embodiments presently contemplated forcarrying out the invention.

In the drawings:

FIG. 1 is a schematic representation of a motor assembly contemplatedfor carrying out the invention.

FIG. 2 is a block diagram of a controller in accordance with theinvention.

FIG. 3 is a block diagram of a controller for configuring of a Wienerfilter in accordance with an embodiment of the invention.

FIG. 4 is a block diagram of a controller for performing fault detectionusing current noise cancellation in accordance with an embodiment of theinvention.

FIG. 5 is a graphical representation of plotted fault index datarelative to fault index thresholds according to a statistical processcontrol technique in accordance with an embodiment of the invention.

FIG. 6 is a block diagram of a controller in accordance with anotherembodiment of the invention.

FIG. 7 is a flow chart illustrating a technique for fault detectionusing current noise cancellation in accordance with an embodiment of theinvention.

FIG. 8 is a flow chart illustrating a technique for fault detectionusing current noise cancellation in accordance with another embodimentof the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The embodiments of the invention set forth herein relate to thedetection of abnormal conditions to predictively determine potentialmotor faults. Current signature analysis (CSA) is utilized to review rawdata received from a plurality of sensors of a controller monitoring anoperating motor. The system, which is preferably disposed within thecontroller, decomposes the sensed/monitored current into a non-faultcomponent and a fault component, and performs a noise-cancellationoperation to isolate the fault component of the current and generate afault identifier. An operator of the monitored motor system is thenproactively alerted of a potential fault prior to a fault occurrence.

Referring now to FIG. 1, a motor assembly, such as an induction motor,is configured to drive a load. The motor assembly 10 includes a motor 12that receives power from a power supply 14. The motor assembly 10 alsoincludes a controller 16 (i.e., current monitoring system) used tomonitor, as well as control, operation of the motor 10 in response tooperator inputs or motor fault conditions. The motor 12 and thecontroller 16 typically are coupled to electronic devices such as apower controller, or starter 17, and are in series with the motor supplyto control power to the motor 12. The controller 16 includes a processor18 that, as will be described in greater detail with respect to FIG. 2,implements an algorithm to determine the presence of unwanted mechanicalconditions and predictively alert an operator of a potential faultbefore a fault occurs. The controller 16 further includes currentsensors 22. According to an exemplary embodiment of the invention, it isunderstood that current sensors 22 are existing sensors used to alsomonitor current input to the motor and generally monitor motoroperation. That is, a separate set of current sensors for acquiringcurrent data for use in the noise-cancellation system/technique of theinvention (described in detail below) are not required. Thus, theacquisition of current data via current sensors 22 for use in thenoise-cancellation system/technique is understood to form a “sensorless”current monitoring system/technique for predictively determiningpotential motor faults. As is generally known, current data may beacquired from only two of the phases of a three-phase motor as currentdata for the third phase may be extrapolated from the current data ofthe monitored two phases. While the present invention will be describedwith respect to a three-phase motor, the present invention isequivalently applicable to other motors. Additionally, while shown asincluding a pair of current sensors 22, it is also envisioned that asingle current sensor could be used to acquire only one phase ofcurrent.

In one embodiment of the invention, current sensors 22 acquire statorcurrent data from an induction motor. The stator current data acquiredfrom sensors 22 is communicated to processor 18, where the current isanalyzed using CSA to detect incipient (i.e., pending) motor faults,such as a bearing fault. As the identification of characteristic faultfrequencies is not a viable solution for detection of all types ofbearing faults (e.g., generalized roughness faults), according to anembodiment of the invention, processor 18 is programmed to treat thefault detection problem as a low signal-to-noise ratio (SNR) problem.Processor 18 is thus programmed to decompose the stator current intonoise components and fault components (i.e., the bearing fault signal).The noise components are the dominant components in the stator current,and include supply fundamental frequencies and harmonics, eccentricityharmonics, slot harmonics, saturation harmonics, and other componentsfrom unknown sources, including environmental noises. Since thesedominant components exist before and after the presence of a bearingfault, a large body of the information they contain is not related tothe fault. In this sense, they can be treated as “noise” for the bearingfault detection problem. As the “noise” could be 10⁴ times stronger thanthe bearing fault signal (i.e., tens of Amperes vs. milli-Amperes), thedetection of the bearing fault signal constitutes a low SNR problem. Forsolving the low SNR problem, processor 18 implements a noisecancellation technique/process for detecting the bearing fault signal.The noise components in the stator current are estimated and thencancelled by their estimates in a real-time fashion, thus providing afault indicator from the remaining components.

While processor 18 is shown as being included in a stand-alonecontroller 16, it is also recognized that processor 18 could be includedin power control/starter 17. Additionally, it is recognized thatprocessor 18 could be included in another power control device such as ameter, relay, or drive. That is, it is understood that controller 16could comprise an existing power control device, such as a meter, relay,starter, or motor drive, and that processor 18 could be integratedtherein.

Referring now to FIG. 2, a more detailed block diagram of controller 16is shown. As stated with respect to FIG. 1, the controller 16 includesprocessor 18 and current sensors 22. Furthermore, the relay assembly 16includes a notch filter 24, a low pass filter 26, and an analog todigital (A/D) convertor 28. The notch filter 24, low pass filter 26, andA/D convertor 28 operate to receive raw data generated by currentsensors 22 and prepare the raw data for processing by processor 18. Thatis, filters 24 and 26 are used to eliminate the fundamental frequency(e.g., 60 Hz in US and 50 Hz in Asia) and low frequency harmonics, asthese harmonic contents are not related to bearing failure. Removingsuch frequencies (especially the base frequency component) from themeasured current data can greatly improve the analog-to-digitalconversion resolution and SNR, as the 60 Hz frequency has a largemagnitude in the frequency spectrum of the current signal. Whilecontroller 16 is shown as including filters 24, 26, it is alsoenvisioned, however, that current data could be passed directly fromcurrent sensors 22 to the A/D convertor 28.

As shown in FIG. 2, processor 18 functions, at least in part, as a noisecancellation system that decomposes the stator current into noisecomponents and fault components. Processor 18 thus includes an inputdelay 30 and a current predictor 32, with the current predictor 32configured to predict noise components present in the stator current.Subtracting the prediction of the noise components from repeatedlyacquired real-time stator current yields fault components which areinjected into the stator current by bearing failures/faults. It isenvisioned that current predictor 32 can be configured as a Wienerfilter (infinite impulse response (IIR) or fixed impulse response(FIR)), a steepest descent algorithm, a least mean square (LMS)algorithm, a least recursive squares (LRS) algorithm, or other digitalfilter.

Referring now to FIG. 3, in an exemplary embodiment of the invention,processor 34 includes therein a Wiener filter 36 that provides for noisecancellation in the stator current and isolation of a fault signaltherein. To provide accurate noise cancellation in the stator current,processor 34 is programmed to configure Wiener filter 36 to accuratelydefine (i.e., estimate) most noise components in the stator current,such that the fault signal in the stator current is not included in itsoutput. In configuring the Wiener filter 36, processor 34 analyzes thestator current data associated with healthy bearing conditions. Thisstator current data associated with healthy bearing conditions caninclude a first set of stator current data that is acquired, forexample, within a short period after the installation of a bearing or atthe start of a bearing condition monitoring process, thus ensuring thatno bearing fault component is included in the stator current. The firstset of stator current data thus comprises baseline current data thatessentially contains pure noise data that does not include faultinformation.

The first set of stator current data, or baseline current data, isreceived by processor 34 and is implemented for configuring Wienerfilter 36. More specifically, the baseline current data is used forassigning coefficients in the Wiener filter 36. Processor 34 assignscoefficients to the Wiener filter 36 such that the prediction error,e(n), of the filter is minimized in the mean-square sense. As shown inFIG. 3, the baseline current data is described by:x(n)=d ₁(n)+d(n)+v ₁(n)  [Eqn. 1],where d₁(n) is the noise components, d(n) is the fault signal, and v₁(n)is the measurement noise. As set forth above, the baseline current datais devoid of a fault signal, and as such, Eqn. 1 reduces tox(n)=d₁(n)+v₁(n).

In configuring the Wiener filter 36, the processor 34 assigns thecoefficients of the filter by using the minimum mean-squared error(MMSE) method. In implementing/applying the MMSE method, processor 34solves for the coefficients, w(k), k=0, 1, . . . , p, to minimize themean square prediction error, ξ, according to:

$\begin{matrix}{{\xi = {{E\{ {{e(n)}}^{2} \}} = {E\{ {{{x(n)} - {\sum\limits_{k = 0}^{p}{{w(k)}{x( {n - n_{0} - k} )}}}}}^{2} \}}}},} & \lbrack {{Eqn}.\mspace{14mu} 2} \rbrack\end{matrix}$where E{ } is the expected value, n₀ is the delay of the input x(n),w(k), k=0, 1, . . . , p are the coefficients of the Wiener filter 36,and p is the order of the filter.

The coefficients are found by setting the partial derivatives of ξ withrespect to w(k) equal to zero, as follows:

$\begin{matrix}{{{\frac{\partial\xi}{\partial{w(k)}} = {{E\frac{\partial{{\mathbb{e}}^{2}(n)}}{\partial{w(k)}}} = {{E\{ {2\;{e(n)}\frac{\partial{e(n)}}{w(k)}} \}} = 0}}};}{{k = 0},1,\ldots\mspace{14mu},{p..}}} & \lbrack {{Eqn}.\mspace{14mu} 3} \rbrack\end{matrix}$

Substituting:

$\begin{matrix}{{\frac{\partial{e(n)}}{\partial{w(k)}} = {- {x( {n - n_{0} - k} )}}},} & \lbrack {{{Eqn}.\mspace{14mu} 3}a} \rbrack\end{matrix}$into Eqn. 3 yields:E{e(n)x(n−n ₀ −k)}=0; k=0,1, . . . , p  [Eqn 4],which is known as the orthogonality principle or the projection theorem.Substituting the equation:

$\begin{matrix}{{e(n)} = {{x(n)} - {\sum\limits_{j = 0}^{p}{{w(j)}{x( {n - n_{0} - j} )}}}}} & \lbrack {{{Eqn}.\mspace{14mu} 4}a} \rbrack\end{matrix}$into Eqn. 4 yields:

$\begin{matrix}{{{{E\{ {\lbrack {{x(n)} - {\sum\limits_{j = 0}^{p}{{w(j)}{x( {n - n_{0} - j} )}}}} \rbrack{x( {n - n_{0} - k} )}} \}} = 0};}{{k = 0},1,\ldots\mspace{14mu},p,}} & \lbrack {{Eqn}.\mspace{14mu} 5} \rbrack\end{matrix}$or equivalently:

$\begin{matrix}{{{{\sum\limits_{j = 0}^{p}{{w(j)}E\begin{Bmatrix}{x( {n - n_{0} - j} )} \\{x( {n - n_{0} - k} )}\end{Bmatrix}}} = {E\{ {{x(n)}{x( {n - n_{0} - k} )}} \}}};}{{k = 0},1,\ldots\mspace{14mu},{p.}}} & \lbrack {{Eqn}.\mspace{14mu} 6} \rbrack\end{matrix}$

By assuming that the signal x(n) is wide-sense stationary (WSS), then:E{x(n−j)x(n−k)}=r _(x)(k−j)  [Eqn. 7].Eqn. 6 is thus simplified to:

$\begin{matrix}{{{{\sum\limits_{j = 0}^{p}{{w(j)}{r_{x}( {k - j} )}}} = {r_{x}( {n_{0} + k} )}};{k = 0}},1,\ldots\mspace{14mu},{p.}} & \lbrack {{Eqn}.\mspace{14mu} 8} \rbrack\end{matrix}$In matrix form, Eqn. 8 can be written as:

$\begin{matrix}{{{\begin{bmatrix}{r_{x}(0)} & {r_{x}(1)} & \ldots & {r_{x}(p)} \\{r_{x}(1)} & {r_{x}(0)} & \ldots & {r_{x}( {p - 1} )} \\\vdots & \vdots & \; & \vdots \\{r_{x}(p)} & {r_{x}( {p - 1} )} & \ldots & {r_{x}(0)}\end{bmatrix}\begin{bmatrix}{w(0)} \\{w(1)} \\\vdots \\{w(p)}\end{bmatrix}} = \begin{bmatrix}{r_{x}( n_{0} )} \\{r_{x}( {n_{0} + 1} )} \\\vdots \\{r_{x}( {n_{0} + p} )}\end{bmatrix}},,} & \lbrack {{Eqn}.\mspace{14mu} 9} \rbrack\end{matrix}$or denoted by:R_(x)w=r.  [Eqn. 10].

The autocorrelation sequences in Eqn. 9 can be estimated by timeaverages when implementing this method. For finite data records (i.e., afinite number of stator current data points), x(n), 0≦n≦N−1, theautocorrelation sequences can be estimated by:

$\begin{matrix}{{{\hat{r}}_{x}(k)} = {\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}{{x(n)}{{x( {n - k} )}.}}}}} & \lbrack {{Eqn}.\mspace{14mu} 11} \rbrack\end{matrix}$The matrix R_(x) is a symmetric Toeplitz matrix and can be solvedefficiently by the Levinson-Durbin Recursion algorithm.

As shown in FIG. 3, the output of Wiener filter 36 is a prediction,g(n), of the stator current, with the prediction error, e(n), of thefilter being defined as the measured baseline current data, x(n), minusthe predicted stator current g(n). As set forth above, the coefficientsof the Wiener filter 36 are assigned such that the prediction error e(n)is minimized in the mean square sense (i.e., e(n)≈0). Thus, as thebaseline current data is composed of essentially just a noise component,d₁(n)+v₁(n), the predicted stator current g(n) should also be comprisedof essentially just a noise component, forming a predicted noisecomponent, d₁^(n)+v₁^(n), that can be used in continued real-timemonitoring of the bearing condition for noise cancellation in repeatedlyacquired stator current data and for identification of bearing faultsignals.

As set forth above, upon configuring of Wiener filter 36 (i.e., settingof the Wiener filter coefficients) from the previous samples of thestator current (i.e., the baseline current data), processor 34 is ableto accurately detect bearing fault conditions in the motor system byestimating the noise components in additionally acquired stator currentin real-time. As the dominant noise components (sinusoidal) in thestator current essentially do not change at constant loads, either inmagnitude or in frequency, they can therefore be predicted in the mostrecent samples (i.e., real-time samples) of the stator current that isbeing monitored. Thus, in monitoring operation of the induction motor 12(FIG. 1), additional sets of stator current data are acquired by sensors22 and received by processor 34 for performing a stator current noisecancellation thereon. The additional sets of stator current data, aredefined by:x(n)=d ₁(n)+d(n)+v ₁(n),  [Eqn. 11a],where d₁(n) is the noise components, d(n) is the fault signal, and v₁(n)is the measurement noise, as set forth above in Eqn. 1. As shown in FIG.4, in monitoring current data from the inductor motor, the statorcurrent is passed through input delay 30, which provides a delay z of n₀samples and through Wiener filter 36, to cancel the estimated noisecomponents d₁^(n)+v₁^(n) therefrom and produce a noise-cancelled statorcurrent. From FIG. 4, it can be seen that if the Wiener filter 36 has agood performance (i.e., d₁^(n)+v₁^(n) is close to d₁(n)+v₁(n)), theremaining part, y(n), of the stator current after noise cancellation(i.e., residual current data) will be the fault signal d(n). That is,when a bearing fault develops, the Wiener filter 36 predicts and cancelsonly the noise components in the stator current and keeps any remainingresidual current data intact during the noise canceling process, fromwhich fault components d(n) are identified. It is noted that the faultcomponents d(n) are comprised of a plurality of fault characteristicsfrom across a fault frequency spectrum. That is, a plurality of faultfrequencies having a plurality of fault amplitudes comprise the faultcomponents, and a collective effect of these frequencies and amplitudesare encompassed by the isolated fault components to facilitate the faultdetection. As the frequencies of the fault signal and the magnitudes ofthe fault components are small for generalized roughness bearing faults,the summation of these factors in the collective fault component d(n)allows for increased fault signal strength and improved bearing faultdetection.

From the isolated fault components remaining in the noise-cancelledstator current, a fault index (i.e., fault indicator) is determined byprocessor 34. In an exemplary embodiment, the fault index is calculatedas the root mean square (RMS) value of the noise-cancelled statorcurrent. Taking the RMS value of the isolated fault components providesfor a larger fault signal that can be monitored, allowing for improvedrecognition of bearing faults. Processor 34 is further programmed toanalyze the fault index to determine if the fault index exceeds athreshold. If the fault index exceeds the threshold, then processor 34generates an alert (i.e., audible or visual alert) to inform an operatorthat a fault component in the stator current has exceeded a desiredamount. The operator can thus shut down operation of the motor at aconvenient time to further assess the bearings. Alternatively, or inaddition thereto, the fault information and its severity can also becommunicated to a centralized monitoring system (not shown), such as aComputerized Maintenance Management System (CMMS) or distributed controlsystem (DCS).

In an exemplary embodiment of the invention, a Statistical ProcessControl (SPC) technique is applied to analyze a plurality of faultindices and set a “threshold” based thereon. With respect to analyzingthe fault components present in the stator current to detect generalizedroughness bearing faults, it is difficult to relate these faultcomponents in the stator current to the bearing fault severity. That is,the lack of equations available to describe fault signatures in statorcurrent injected by generalized roughness bearing faults and thesubtleness of bearing fault signatures in stator current makes itdifficult to pre-define fault severity levels. As such, a SPC techniqueis applied to establish a warning threshold based on the statistics ofthe fault signal in the specific current monitoring process, rather thanpre-setting a pre-defined, universal threshold for all applications. TheSPC technique distinguishes abnormal changes in the noise-cancelledstator current (and resulting fault indices) that are caused by abearing fault from ambient changes.

Referring to FIG. 5, application of the SPC technique to the generatedfault indices obtained via stator current noise cancellation is shown.Each fault index is plotted to an X chart 35, displaying individualfault index values 37, and a mR (i.e., “moving Range”) chart 38 formonitoring differences 39 between the values of the fault indices. Theindividual measurements 37 are plotted on the X chart 35 and thedifferences 39 (i.e., moving range) are plotted on the mR chart 38.Based on the plotted values, upper and lower control limits 41, 43 aredetermined from the X chart 35 and an upper control limit 45 isdetermined for the mR chart 38. For bearing fault detection, since theSPC is applied to noise-cancelled stator current, a fault index valuefalling below a lower control limit indicates a better bearing conditionand, therefore, it is not of concern. As such, the upper control limit41 of the X chart 35 and/or the upper control limit 45 of the mR chart38 comprise the relevant control limit (i.e., fault index warningthreshold) for determining a threshold exceedance. In one embodiment,the upper control limits 41, 45 can be set at three standard deviationsfrom the mean fault index value 47 and at three standard deviations fromthe mean difference value between adjacently acquired fault indices 49,respectively.

Upon calculation of the control limits 41, 45 via the SPC technique, thefault indices are analyzed with respect to these control limits.Detection of an uncontrolled variation in the fault indices isindicative of a deteriorated bearing condition. That is, if the analyzedfault indices begin to frequently exceed the control limits 41, 45, sucha variation is indicative of a deteriorated bearing condition (i.e.,incipient bearing fault). Thus, in determining whether a deterioratedbearing condition exists that necessitates generation of an alert, thepercentage of fault indices exceeding the upper control limit 41, 45 isexamined. If that percentage exceeds a pre-determined percentage, thenit is determined that a deteriorated bearing condition exists and analert is generated. For example, if the percentage of fault indicesfalling outside (i.e., exceeding) the control limits 41, 45 is above10%, then an alert is generated. A SPC technique is thus utilized tomonitor the fault indices obtained in real-time and analyze the faultindex values to determine if a “threshold” has been exceeded, thusallowing for a determination that a deteriorated bearing condition orbearing fault exists.

Referring now to FIG. 6, in another embodiment of the invention, thefault information d(n) in the stator current isolated by the noisecancellation technique of processor 40 can be viewed as the predictionerror e(n) of a prediction error filter (PEF) 42. That is, when thebearing fault develops and the condition of the system changes, theprediction error increases. As shown in FIG. 6, if the noisecancellation system/technique is viewed as a PEF 42, then the systemperformance can be measured by the prediction error of the filter. Thatis, to have good performance, the prediction error should besignificantly larger for a faulted bearing condition than for a healthybearing condition. Consequently, the prediction error shown in FIG. 6gets larger when the system enters a bearing fault condition from ahealthy bearing condition.

In examining the PEF 42 to assess a prediction error, a general equationdescribing the prediction error can be given, along with specificequations for the filter performance for a healthy-bearing condition andfor the filter performance having a bearing-fault condition. Bydefinition, a general equation for the mean-square prediction error ofthe filter is:

$\begin{matrix}{\xi = {E{\{ {{{x(n)} - {\sum\limits_{k = 0}^{p}{{w(k)}{x( {n - n_{0} - k} )}}}}}^{2} \}.}}} & \lbrack {{Eqn}.\mspace{14mu} 12} \rbrack\end{matrix}$

This is the same error as in Eqn. 2, which was minimized to find thecoefficients of the Wiener filter. Upon expansion, the above equationcan be rewritten as:

$\begin{matrix}{\xi = {\begin{bmatrix}{{r_{x}(0)} -} \\{\sum\limits_{k = 0}^{p}{{w(k)}{r_{x}( {n_{0} + k} )}}}\end{bmatrix} + {\sum\limits_{k = 0}^{p}{{{w(k)}\begin{bmatrix}{{\sum\limits_{j = 0}^{p}{w(j)r_{x}( {k - j} )}} -} \\{r_{x}( {n_{0} + k} )}\end{bmatrix}}.}}}} & \lbrack {{Eqn}.\mspace{14mu} 13} \rbrack\end{matrix}$

Since the PEF 42 is designed to minimize the error in Eqn. 12 by usinghealthy bearing data, this prediction error is small for ahealthy-bearing condition. In fact, for a healthy-bearing condition,since w(k), k=0, 1, . . . , p, are solutions to Eqn. 8, the second termof the right hand side of Eqn. 13 is zero. Therefore, the predictionerror for a healthy-bearing condition is:

$\begin{matrix}{\xi_{\min} = {{r_{x}(0)} - {\sum\limits_{k = 0}^{p}{{w(k)}{{r_{x}( {n_{0} + k} )}.}}}}} & \lbrack {{Eqn}.\mspace{14mu} 14} \rbrack\end{matrix}$

At such a condition, since x(n)=d₁(n)+v₁(n), therefore, it follows that:

$\begin{matrix}\begin{matrix}{{r_{x}(k)} = {E\{ {{x(n)}{x( {n + k} )}} \}}} \\{= {E\{ {\lbrack {{d_{1}(n)} + {v_{1}(n)}} \rbrack\lbrack {{d_{1}( {n + k} )} + {v_{1}( {n + k} )}} \rbrack} \}}} \\{= {{E\{ {{d_{1}(n)}{d_{1}( {n + k} )}} \}} + {E\{ {{d_{1}(n)}{v_{1}( {n + k} )}} \}} +}} \\{{E\{ {{v_{1}(n)}{d_{1}( {n + k} )}} \}} + {E{\{ {{v_{1}(n)}{v_{1}( {n + k} )}} \}.}}}\end{matrix} & \lbrack {{Eqn}.\mspace{14mu} 15} \rbrack\end{matrix}$

Since d₁(n) and v₁(n) are jointly wide sense stationary (WSS), Eqn. 15becomes:r _(x)(k)=r _(d) ₁ (k)+2r _(d) ₁ _(v) ₁ (k)+r _(v) ₁ (k)  [Eqn. 16].

Since the measurement noise v₁(n) is random, its power spectrum isdistributed over a broad frequency range, its autocorrelation ispulse-like, and its cross-correlations with other signals are zero.(i.e., the autocorrelation sequences of a signal are the inverse Fouriertransform of its power spectrum by definition). It thus follows fromEqn. 16 that:r _(x)(0)=r _(d) ₁ (0)+r _(v) ₁ (0), r _(x)(k)=r _(d) ₁ (k),k≠0  [Eqn.17].

Substituting Eqn. 17 into Eqn. 14 yields:

$\begin{matrix}{\xi_{\min} = {{r_{d_{1}}(0)} + {r_{v_{1}}(0)} - {\sum\limits_{k = 0}^{p}{{w(k)}{{r_{d_{1}}( {n_{0} + k} )}.}}}}} & \lbrack {{Eqn}.\mspace{14mu} 18} \rbrack\end{matrix}$

To further investigate the performance of the system, the noisecomponents (including the supply fundamental and harmonics, theeccentricity harmonics, the slot harmonics, etc.) are described as:

$\begin{matrix}{{{d_{1}(n)} = {\sum\limits_{m = 1}^{M}{A_{m}{\sin( {{\omega_{m}n} + \varphi_{m}} )}}}},,} & \lbrack {{Eqn}.\mspace{14mu} 19} \rbrack\end{matrix}$where A_(m), ω_(m), φ_(m), m=1, . . . , M, are the amplitudes, thefrequencies, and the angles of M noise components in the stator current.To compute the autocorrelation sequences of the signal d₁(n), thefollowing relationship is defined:

$\begin{matrix}\begin{matrix}{{r_{d_{1}}(k)} = {E\{ {{d_{1}(n)}{d_{1}( {n + k} )}} \}}} \\{= {E\{ \lbrack {\sum\limits_{m = 1}^{M}{A_{m}{{\sin( {{\omega_{m}n} + \varphi_{m}} \rbrack}\lbrack {\sum\limits_{j = 1}^{M}{A_{j}{\sin\lbrack {{\omega_{j}( {n + k} )} + \varphi_{j}} \rbrack}}} \rbrack}}} \} }} \\{= {{E\{ {\sum\limits_{m = 1}^{M}{A_{m}^{2}{\sin( {{\omega_{m}n} + \varphi_{m}} )}{\sin\lbrack {{\omega_{m}( {n + k} )} + \varphi_{m}} \rbrack}}} \}} +}} \\{E{\{ {\sum\limits_{m = 1}^{M}{A_{m}{\sum\limits_{{j = 1},{j \neq m}}^{M}{A_{j}{\sin( {{\omega_{m}n} + \varphi_{m}} )}{\sin\lbrack {{\omega_{j}( {n + k} )} + \varphi_{j}} \rbrack}}}}} \}.}}\end{matrix} & \lbrack {{Eqn}.\mspace{14mu} 20} \rbrack\end{matrix}$

Eqn. 20 is then reduced by recognizing the following relationships:

$\begin{matrix}{{{E\begin{Bmatrix}{\sum\limits_{m = 1}^{M}{A_{m}^{2}{\sin( {{\omega_{m}n} + \varphi_{m}} )}}} \\{\sin\lbrack {{\omega_{m}( {n + k} )} + \varphi_{m}} \rbrack}\end{Bmatrix}} = {{\sum\limits_{m = 1}^{M}{\frac{A_{m}^{2}}{2}E\begin{Bmatrix}{{\cos( {\omega_{m}k} )} -} \\{\cos\begin{pmatrix}{{2\;\omega_{m}n} +} \\{{\omega_{m}k} + {2\;\varphi_{k}}}\end{pmatrix}}\end{Bmatrix}}} = {\sum\limits_{m = 1}^{M}{\frac{A_{m}^{2}}{2}{\cos( {\omega_{m}k} )}}}}}{and}} & \lbrack {{Eqn}.\mspace{14mu} 21} \rbrack \\{{E\begin{Bmatrix}{\sum\limits_{m = 1}^{M}{A_{m}{\sum\limits_{{j = 1},{j \neq m}}^{M}{A_{j}{\sin( {{\omega_{m}n} + \varphi_{m}} )}}}}} \\{\sin\lbrack {{\omega_{j}( {n + k} )} + \varphi_{j}} \rbrack}\end{Bmatrix}} = {{\frac{1}{2}{\sum\limits_{m = 1}^{M}{A_{m}{\sum\limits_{{j = 1},{j \neq m}}^{M}{A_{j}\langle {{E\{ {\cos\begin{bmatrix}{{( {\omega_{j} - \omega_{m}} )n} +} \\{{\omega_{j}k} + ( {\varphi_{j} - \varphi_{m}} )}\end{bmatrix}} \}} - {E\{ {\cos\begin{bmatrix}{{( {\omega_{j} + \omega_{m}} )n} +} \\{{\omega_{j}k} + ( {\varphi_{j} + \varphi_{m}} )}\end{bmatrix}} \}}} \rangle}}}}} = 0.}} & \lbrack {{Eqn}.\mspace{14mu} 22} \rbrack\end{matrix}$

Therefore, the autocorrelation sequences of the signal d₁(n) are:

$\begin{matrix}{{r_{d_{1}}(k)} = {\sum\limits_{m = 1}^{M}{\frac{A_{m}^{2}}{2}{{\cos( {\omega_{m}k} )}.}}}} & \lbrack {{Eqn}.\mspace{14mu} 23} \rbrack\end{matrix}$

Substituting Eqn. 23 into Eqn. 18 yields the prediction error of thefilter 42 for a healthy-bearing condition as:

$\begin{matrix}{\xi_{\min} = {{\sum\limits_{m = 1}^{M}{\frac{A_{m}^{2}}{2}\{ {1 - {\sum\limits_{k = 0}^{p}{{w(k)}{\cos\lbrack {\omega_{m}( {n_{0} + k} )} \rbrack}}}} \}}} + {{r_{v_{1}}(0)}.}}} & \lbrack {{Eqn}.\mspace{14mu} 24} \rbrack\end{matrix}$

Similarly, for a faulty bearing condition, the mean square predictionerror can still be calculated from Eqn. 13. For convenience, Eqn. 13 isrepeated here as:

$\begin{matrix}{\xi = {\begin{bmatrix}{{r_{x}(0)} - {\sum\limits_{k = 0}^{p}{w(k)}}} \\{r_{x}( {n_{0} + k} )}\end{bmatrix} + {\sum\limits_{k = 0}^{p}{{{w(k)}\begin{bmatrix}{{\sum\limits_{j = 0}^{p}{w(j)r_{x}( {k - j} )}} -} \\{r_{x}( {n_{0} + k} )}\end{bmatrix}}.}}}} & \lbrack {{Eqn}.\mspace{14mu} 25} \rbrack\end{matrix}$

However, different from the situation of a healthy-bearing condition,the second term on the right hand side of Eqn. 25 for a faulty-bearingcondition is not zero because of the presence of the fault signal in thestator current, which is x(n)=d₁(n)+d(n)+v₁(n). It follows that:r _(x)(k)=E{x(n)x(n+k)}=E{[d ₁(n)+d(n)+v ₁(n)][d ₁(n+k)+d(n+k)+v₁(n+k)]}  [Eqn. 26].

Assuming d₁(n), d(n), and v₁(n) are jointly WSS, then Eqn. 26 becomes:r _(x)(k)=r _(d) ₁ (k)+r _(d)(k)+r _(v) ₁ (k)+2r _(d) ₁ _(v) ₁ (k)+2r_(d) ₁ _(d)(k)+2r _(dv) ₁ (k)  [Eqn. 27].

As for a healthy-bearing condition, if it is assumed that themeasurement noise v₁(n) is a broadband signal and not correlated withd₁(n) and d(n), it then follows that:r _(x)(0)=r _(d) ₁ (0)+r _(d)(0)+r _(v) ₁ (0)+2r _(d) ₁ _(d)(0)  [Eqn.28],and that:r _(x)(k)=r _(d) ₁ (k)+r _(d)(k)+2r _(d) ₁ _(d)(k), k≠0  [Eqn. 29].

If the noise components are described as:

$\begin{matrix}{{{d_{1}(n)} = {\sum\limits_{m = 1}^{M}{A_{m}{\sin( {{\omega_{m}n} + \varphi_{m}} )}}}},} & \lbrack {{{Eqn}.\mspace{14mu} 29}a} \rbrack\end{matrix}$as set forth in Eqn. 19, then the fault components can be described as:

$\begin{matrix}{{{d(n)} = {\sum\limits_{q = 1}^{Q}{B_{q}{\sin( {{\omega_{q}n} + \varphi_{q}} )}}}},} & \lbrack {{Eqn}.\mspace{14mu} 30} \rbrack\end{matrix}$where A_(q), ω_(q), φ_(q), q=1, . . . , Q, are the amplitudes, thefrequencies, and the angles of Q fault components in the stator currentinjected by a bearing fault. The autocorrelation sequences of d(n) canthus be calculated as in Eqns. 20 to 23, with the result being:

$\begin{matrix}{{r_{d}(k)} = {\sum\limits_{q = 1}^{Q}{\frac{B_{q}^{2}}{2}{{\cos( {\omega_{q}k} )}.}}}} & \lbrack {{Eqn}.\mspace{14mu} 31} \rbrack\end{matrix}$

For ω_(q)≠ω_(m), q=1, 2, . . . , Q, m=1, 2, . . . , M, following thesame steps as in Eqns. 20 to 23, the cross-correlation sequences betweenthe noise components and the fault components become:r _(d) ₁ _(d)(k)=0, k:integer  [Eqn. 32].

Thus, combining Eqns. 25 to 32, the prediction error for afaulty-bearing condition can be obtained as:

$\begin{matrix}{{\xi = {\xi_{\min} + {\sum\limits_{q = 1}^{Q}{\frac{B_{q}^{2}}{2}\{ {1 - {\sum\limits_{k = 0}^{p}{{w(k)}{\cos\lbrack {\omega_{q}( {n_{0} + k} )} \rbrack}}}} \}}} + {\sum\limits_{q = 1}^{Q}{\frac{B_{q}^{2}}{2}\{ {\sum\limits_{k = 0}^{p}{{w(k)}\begin{bmatrix}{\sum\limits_{j = 0}^{p}{{w(j)}{\cos( {{\omega_{q}( {k - j} )} -} }}} \\{\cos( {\omega_{q}( {n_{0} + k} )} )}\end{bmatrix}}} \}}}}},} & \lbrack {{Eqn}.\mspace{14mu} 33} \rbrack\end{matrix}$where ξ_(min) is the prediction error for a healthy-bearing conditionexpressed in Eqn. 24.

Beneficially, it is noted that the noise cancellation method set forthabove considers a collective effect of the fault components tofacilitate the fault detection. That is, as the frequencies of the faultsignal, ω_(q)'s, and the magnitudes of the fault components, B_(q)'s,are small for generalized roughness bearing faults, summing thesefactors in the collective fault component d(n), along with the bearingcontact angle φ_(q), allows for increased fault signal strength andimproved bearing fault detection. It is further noted that if the faultsignal, d(n), is a broadband signal, then it has the same effect as thebroadband measurement noise v₁(n), and since the power of the broadbandsignal remains in the prediction error (both for a healthy-bearingcondition and for a faulty-bearing condition), the presence of the faultsignal results in an increase of the prediction error.

Additionally, even if ω_(q)=ω_(m) and there is a smaller increase in theprediction error, (since the third term on the right hand side of Eqn.33 is zero, while the second term is nonzero), fault information isstill conserved in the resulting prediction error. That is, even if thefault components and the noise components have common frequencies, suchas when the bearing fault augments the dynamic eccentricity of themotor, the fault information is still conserved in the resultingprediction error. The above features thus provide for an improvedcurrent-based sensing technique to detect generalized roughness bearingfaults.

Referring now to FIG. 7, a flow chart illustrating a current-basedtechnique 46 for detecting generalized roughness bearing faults isdisplayed. The technique 46 begins by acquiring and receiving a firstset of stator current data, x(n), from an electrical machine, such as athree-phase induction motor, to produce baseline current data 48. Thefirst set of stator current data that is acquired/received is comprisedof stator current data associated with healthy bearing conditions, whichis known to be devoid of any bearing fault signal therein.

From the baseline current data, a current data filter (i.e., noisecancellation system) is configured 50 to provide noise cancellation tothe stator current, so as to isolate any fault component presenttherein. In an exemplary embodiment, the current data filter is a Wienerfilter that is designed to cancel the noise component from the statorcurrent based on a filtering of received stator current data by anestimation of the noise component in the stator current. To provideaccurate noise cancellation in the stator current, the Wiener filter isconfigured such that it can accurately estimate most noise components inthe stator current and such that the fault signal in the stator currentis not included in its output. In configuring the Wiener filter, thebaseline current data is used for assigning coefficients in the Wienerfilter, such that no bearing fault information is embedded in thecoefficients. The Wiener filter is designed such that a prediction errorthereof is minimized in the mean square sense. That is, the coefficientsare assigned using the minimum mean-squared error (MMSE) method. As theWiener filter is configured based on the baseline current data (i.e.,pure noise current data), this means that the output of the Wienerfilter is a predicted noise component, g(n), of the stator current thatis essentially equal to the baseline current data, such that theprediction error is minimized, i.e., e(n)=x(n)−g(n).

After configuring of the Wiener filter, the technique continues byacquiring and receiving at least one additional set of stator currentdata 52. The additional stator current data is acquired/received after aperiod of use of the electrical machine and is monitored to detectbearing fault signals present in the stator current. The additional setsof stator current data are passed to the current data filter 54 toperform a noise component cancellation thereon. The estimated noisecomponent provided by the current data filter is cancelled from thestator current 56 to isolate any fault component present in the statorcurrent. That is, as the noise components (sinusoidal) in the statorcurrent essentially do not change at constant loads, either in magnitudeor in frequency, the predicted noise component output from the currentdata filter (and based on the baseline current data) can be cancelledfrom the most recent samples of the stator current (i.e., theadditionally acquired stator current) to accurately determine a faultcomponent in the stator current. Assuming that the current data filterwas properly configured and has good performance, the remaining part ofthe stator current after noise cancellation will accurately portray thefault signal d(n).

From the fault component remaining in the noise-cancelled statorcurrent, a fault index (i.e., fault indicator) is determined 58. In anexemplary embodiment, the fault indicator is calculated as the RMS valueof the noise-cancelled stator current. Taking the RMS value of theisolated fault component provides for a larger signal that can bemonitored, allowing for improved recognition of bearing faults. Uponcalculation, the fault index is compared to additionally calculatedfault indices to generate a fault index threshold 59 and determine ifthe fault indicator exceeds that fault index threshold 60. If the faultindicator does not exceed the fault index threshold 62, then thetechnique proceeds by continuing to receive and monitor additionalstator current data 64. If, however, the fault indicator does exceed thefault index threshold 66, then an alert is generated 68, such as anaudible or visual alert, to inform an operator that a fault component inthe stator current has exceeded a desired amount. The operator is thusallowed to shut down operation of the electrical machine to furtherexamine the bearings for faults.

In an exemplary embodiment of the technique 46, the fault indexthreshold is determined 59 via a SPC technique. The fault indexthreshold (i.e., control limit) is determined for each of an X chart anda mR chart. Upon calculation of the fault index thresholds via the SPCtechnique, the fault indices are analyzed with respect to thesethresholds 60. If a pre-determined amount or percentage of the faultindices fall outside the fault index thresholds 66, it is determinedthat a deteriorated bearing condition exists and an alert is generated68. For example, if the percentage of fault indices falling outside thecontrol limits is above 10%, then an alert can be generated. A SPCtechnique is thus utilized to monitor the fault indices obtained inreal-time and analyze the fault index values to determine if a“threshold” has been exceeded, thus allowing for a determination that adeteriorated bearing condition or bearing fault exists.

Referring now to FIG. 8, a flow chart illustrating a modifiedcurrent-based technique 80 for detecting generalized roughness bearingfaults is displayed, according to another embodiment of the invention.The technique 80 of FIG. 8 is modified from the technique 46 of FIG. 7in that the current data filter employed for cancelling the estimatednoise component from the stator current is reconfigured upon theacquisition/receiving of each additional set of stator current data.That is, the Wiener filter coefficients are recalculated using theequations set forth above to generate a prediction of the stator currentnoise associated with each current data set. By assuming that allbearing fault conditions are indicated by non-periodic noise resident inthe stator current and by reconfiguring the Wiener filter after eachdata collection interval/iteration, periodic noise components associatedwith varying motor conditions, such as varying load and fundamentalfrequency, may be eliminated, thus providing a more accurate estimationof the noise signal present in the real-time stator current. Since theWiener filter is configured to remove only periodic noise, calculatingthe Wiener filter coefficients using real-time stator current data doesnot negatively affect the determination of a bearing fault condition.

As shown in FIG. 8, the technique 80 begins by acquiring and receiving aset of stator current data, x(n), from an electrical machine, such as athree-phase induction motor, to produce current data 82. From the set ofcurrent data, a current data filter (i.e., noise cancellation system) isconfigured 84 to provide noise cancellation to the stator current, so asto isolate any fault component present therein. In an exemplaryembodiment, the current data filter is a Wiener filter that is designedto cancel the periodic noise component from the stator current based ona filtering of received stator current data by an estimation of theperiodic noise component in the stator current. To provide accuratenoise cancellation in the stator current, the Wiener filter isconfigured such that it can accurately estimate most periodic noisecomponents in the stator current and such that the non-periodic faultsignal in the stator current is not included in its output. Inconfiguring the Wiener filter, the real-time stator current data is usedfor assigning coefficients in the Wiener filter. The Wiener filter isdesigned such that a prediction error thereof is minimized in the meansquare sense. That is, the coefficients are assigned using the minimummean-squared error (MMSE) method. As the Wiener filter is configuredbased on the real-time stator current data, this means that technique 80takes into account real-time operating conditions by calculating theWiener filter coefficients to correspond with the real-time statorcurrent data. As described above, the current data filter is repeatedlyconfigured/reconfigured to account for changes in thefrequency/amplitude of the noise components that might result fromvarying motor conditions, thus providing for more accurate estimationand elimination of those periodic noise components.

As further shown in FIG. 8, the set of stator current data is passed tothe current data filter 86 to perform a noise component cancellationthereon. The estimated periodic noise component provided by the currentdata filter is cancelled from the stator current 88 to isolate anynon-periodic fault component present in the stator current. From thefault component remaining in the periodic noise-cancelled statorcurrent, a fault index is determined 90. Upon calculation, the faultindex is compared to additionally calculated fault indices to generate afault index threshold 92 and determine if the fault indicator exceedsthat fault index threshold 94. If the fault indicator does not exceedthe fault index threshold 96, then the technique 80 proceeds bycontinuing to receive and monitor additional stator current data 98. If,however, the fault indicator does exceed the fault index threshold 100,then an alert is generated 102 to inform an operator that a faultcomponent in the stator current has exceeded a desired amount.

As set forth above, it is recognized that the noise component from thestator current may vary in certain instances, such as in theimplementation of embodiments of the invention for use in motor driveapplications and for the application of varying loads to the motor. Insuch applications, the iterative reconfiguring of the current datafilter as set forth in FIG. 8 allows for more accurate cancellation ofthe noise component from the stator current by accounting for variationsin the periodic noise component that occur when the fundamentalfrequency and/or amplitude of the periodic component of the statorcurrent varies during use of the motor.

According to embodiments of the invention, the noise cancellation methodset forth above is able to isolate fault components in the statorcurrent to detect incipient bearing faults without the need fordetermining machine parameters, bearing dimensions, nameplate values, orstator current spectrum distributions. The analysis of thenoise-cancelled stator current (and the fault indices generatedtherefrom) via the use of a SPC technique eliminates the need forknowledge of such machine parameters, bearing dimensions, nameplatevalues, or stator current spectrum distributions. That is, as the noisecancellation method determines control limits and fault index warningthresholds by way of a SPC technique based on acquired fault indexvalues rather than on a set of pre-defined equations describing faultsignatures in the stator current, such information is not needed foranalysis of fault components in the stator current. As thedetermination/acquisition of machine parameters, bearing dimensions,nameplate values, or stator current spectrum distributions can bedifficult and time consuming, the lack of a need for such information inembodiments of the system and method of the invention results in moreefficient current-based bearing fault detection.

While embodiments of the invention described herein are directed todetecting bearing faults, one skilled in the art will recognize that thesystem and method for fault detection set forth above may be used topredictively detect a plurality of potential motor faults. The isolationand analysis of non-periodic fault data in a monitored stator currentallows for the predictive detection of such motor faults within a motor.

A technical contribution for the disclosed method and apparatus is thatit provides for a computer implemented technique for detecting impendingmechanical motor faults by way of current noise cancellation. Currentdata is decomposed into a non-fault component (i.e., noise) and a faultcomponent, and noise-cancellation is performed to isolate the faultcomponent of the current and generate a fault index.

Therefore, according to one embodiment of the present invention, acontroller configured to detect indicia of incipient mechanical motorfaults includes a processor programmed to receive a first set ofreal-time operating current data from a motor during operation, define anoise component present in the first set of real-time operating currentdata, and generate a fault index for the first set of real-timeoperating current data based on any isolated fault components. Theprocessor is further programmed to acquire at least one additional setof real-time operating current data from the motor during operation,redefine the noise component present in each of the at least oneadditional sets of real-time operating current data, remove theredefined noise component from each of the at least one additional setsof real-time operating current data to identify any fault componentspresent therein, and generate a fault index for each of the at least oneadditional sets of real-time operating current data based on anyisolated fault components.

According to another embodiment of present invention, a non-invasivemethod for detecting impending faults in electric machines includesacquiring a plurality of stator current data sets from the electricmachine during operation, configuring a current data filter for each ofthe plurality of stator current data sets, and applying each of thecurrent data filters to its respective stator current data set inreal-time to generate a noise-cancelled stator current. The method alsoincludes determining a fault index from the noise-cancelled statorcurrent for each of the plurality of stator current data sets,monitoring a value of the fault index for each of the plurality ofstator current data sets, and generating an alert if the value of apre-determined number of fault indices exceeds a control limit.

According to yet another embodiment of the present invention, a systemfor monitoring current to predict faults includes at least onenon-invasive current sensor configured to acquire stator current datafrom an operating motor. The system also includes a processor connectedto receive the stator current data from the at least one non-invasivecurrent sensor. The processor is programmed to repeatedly receive a setof real-time operating current data from the at least one non-invasivecurrent sensor, where the set of real-time operating data isrepresentative of real-time motor operation. The processor is furtherprogrammed to define a non-fault component from each of the repeatedlyreceived sets of real-time operating current data, the non-faultcomponent being a periodic component of the real-time operating currentdata, and remove the non-fault component from each of the sets ofreal-time operating current data in real-time to isolate residualcurrent data. The processor is also programmed to process the residualcurrent data to identify possible bearing faults, generate a fault indexfor any identified bearing faults, and generate an alert if the faultindex exceeds a fault index threshold.

The present invention has been described in terms of the preferredembodiment, and it is recognized that equivalents, alternatives, andmodifications, aside from those expressly stated, are possible andwithin the scope of the appending claims.

1. A controller configured to detect indicia of incipient mechanicalmotor faults having a processor programmed to: receive a first set ofreal-time operating current data from a motor during operation; define anoise component present in the first set of real-time operating currentdata; remove the noise component from the first set of real-timeoperating current data to identify any fault components present in thefirst set of real-time operating current data; generate a fault indexfor the first set of real-time operating current data based on anyisolated fault components; acquire at least one additional set ofreal-time operating current data from the motor during operation;redefine the noise component present in each of the at least oneadditional sets of real-time operating current data; remove theredefined noise component from each of the at least one additional setsof real-time operating current data to identify any fault componentspresent therein; and generate a fault index for each of the at least oneadditional sets of real-time operating current data based on anyisolated fault components.
 2. The controller of claim 1 wherein theprocessor is further programmed to redefine the noise component presentin each of the at least one additional sets of real-time operatingcurrent data based on periodic components therein.
 3. The controller ofclaim 2 wherein the periodic components in each of the at least oneadditional sets of real-time operating current data comprise sinusoidalsignals of varying frequency and amplitude.
 4. The controller of claim 1wherein the processor is further programmed to apply a statisticalprocess control analysis to a plurality of generated fault indices tocalculate a fault index warning threshold.
 5. The controller of claim 4wherein the processor is further programmed to generate an alert if adetected variation of the fault index exceeds the fault index warningthreshold.
 6. The controller of claim 5 wherein the processor is furtherprogrammed to: determine a percentage of fault indices that exceed thefault index warning threshold; and generate the alert if the percentageof fault indices that exceed the fault index warning threshold exceed apre-determined percentage.
 7. The controller of claim 1 wherein theprocessor is further programmed to: configure a Wiener filter based onthe first set of real-time operating current data; and reconfigure theWiener filter based on each of the at least one additional sets ofreal-time operating current data, the Wiener filter configured toestimate the redefined noise component corresponding to the first set ofreal-time operating current data and each additional set of real-timeoperating current data.
 8. The controller of claim 7 wherein the faultcomponents comprise a prediction error of the Wiener filter.
 9. Thecontroller of claim 1 wherein the processor is further programmed tocalculate a root-mean-square (RMS) value of the fault components togenerate the fault index.
 10. The controller of claim 1 wherein thefault components comprise non-periodic components in each set ofreal-time operating current data.
 11. A non-invasive method fordetecting impending faults in electric machines comprising: acquiring aplurality of stator current data sets from the electric machine duringoperation; configuring a current data filter for each of the pluralityof stator current data sets; applying each of the current data filtersto its respective stator current data set in real-time to generate anoise-cancelled stator current; determining a fault index from thenoise-cancelled stator current for each of the plurality of statorcurrent data sets; monitoring a value of the fault index for each of theplurality of stator current data sets; and generating an alert if thevalue of a pre-determined number of fault indices exceeds a controllimit.
 12. The method of claim 11 wherein configuring the current datafilter for each of the plurality of stator current data sets comprisesreconfiguring a Wiener filter for each of the plurality of statorcurrent data sets.
 13. The method of claim 12 wherein reconfiguring theWiener filter for each of the plurality of stator current data setscomprises estimating a periodic noise component from each of the statorcurrent data sets.
 14. The method of claim 13 wherein generating thenoise-cancelled stator current for each of the plurality of statorcurrent data sets comprises cancelling the periodic noise componentsfrom each of the plurality of stator current data sets.
 15. The methodof claim 12 further comprising selecting coefficients in eachreconfigured Wiener filter by applying a minimum mean-squared error(MMSE) operation to the stator current data set associated with thereconfigured Wiener filter.
 16. A system for monitoring current topredict faults comprising: at least one non-invasive current sensorconfigured to acquire stator current data from an operating motor; and aprocessor connected to receive the stator current data from the at leastone non-invasive current sensor, the processor programmed to: repeatedlyreceive a set of real-time operating current data from the at least onenon-invasive current sensor, the set of real-time operating datarepresentative of real-time motor operation; define a non-faultcomponent from each of the repeatedly received sets of real-timeoperating current data, the non-fault component being a periodiccomponent of the real-time operating current data; remove the non-faultcomponent from each of the sets of real-time operating current data inreal-time to isolate residual current data; process the residual currentdata to identify possible bearing faults; generate a fault index for anyidentified bearing faults; and generate an alert if the fault indexexceeds a fault index threshold.
 17. The current monitoring system ofclaim 16 wherein the processor is further programmed to repeatedlyreconfigure a Wiener filter based on each of the sets of real-timeoperating current data, the Wiener filter reconfigured to redefine thenon-fault component in each of the sets of real-time operating currentdata.
 18. The current monitoring system of claim 17 wherein theprocessor is further programmed to reconfigure the Wiener filter todefine the periodic component in each of the sets of real-time operatingcurrent data.